





'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_   :       Dict       =     logging.get_logger(__name__)

lowerCAmelCase_   :       List[str]       =     '▁'

lowerCAmelCase_   :       List[Any]       =     {'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_   :       str       =     {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

lowerCAmelCase_   :       List[str]       =     {
    'xlm-roberta-base': 5_12,
    'xlm-roberta-large': 5_12,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_12,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_12,
    'xlm-roberta-large-finetuned-conll03-english': 5_12,
    'xlm-roberta-large-finetuned-conll03-german': 5_12,
}
class 							__SCREAMING_SNAKE_CASE    (lowerCamelCase_ ):






				"""simple docstring"""







				__a  =VOCAB_FILES_NAMES
				__a  =PRETRAINED_VOCAB_FILES_MAP
				__a  =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a  =['input_ids', 'attention_mask']




				def __init__(    self	:   Any   ,							__a	:   Optional[Any]   ,							__a	:   Tuple="<s>"   ,							__a	:   int="</s>"   ,							__a	:   Dict="</s>"   ,							__a	:   List[Any]="<s>"   ,							__a	:   Tuple="<unk>"   ,							__a	:   Union[str, Any]="<pad>"   ,							__a	:   Optional[int]="<mask>"   ,							__a	:   Optional[Any] = None   ,							**__a	:   Dict   ,							):
								_a				      =					AddedToken(__a   ,							lstrip=__a   ,							rstrip=__a   ) if isinstance(__a   ,							__a   ) else mask_token

								_a				      =					{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__a   ,							eos_token=__a   ,							unk_token=__a   ,							sep_token=__a   ,							cls_token=__a   ,							pad_token=__a   ,							mask_token=__a   ,							sp_model_kwargs=self.sp_model_kwargs   ,							**__a   ,							)

								_a				      =					spm.SentencePieceProcessor(**self.sp_model_kwargs   )
								self.sp_model.Load(str(__a   )   )
								_a				      =					vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# Mimic fairseq token-to-id alignment for the first 4 token
								_a				      =					{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

								# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
								_a				      =					1

								_a				      =					len(self.sp_model   ) + self.fairseq_offset
								_a				      =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}




				def __getstate__(    self	:   Dict   ):
								_a				      =					self.__dict__.copy()
								_a				      =					None
								_a				      =					self.sp_model.serialized_model_proto()
								return state




				def __setstate__(    self	:   List[Any]   ,							__a	:   List[str]   ):
								_a				      =					d

								# for backward compatibility
								if not hasattr(self   ,							"sp_model_kwargs"   ):
												_a				      =					{}

								_a				      =					spm.SentencePieceProcessor(**self.sp_model_kwargs   )
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )




				def     UpperCamelCase__  (    self	:   Optional[Any]   ,							__a	:   Optional[int]   ,							__a	:   Optional[Any] = None   ):


								if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								_a				      =					[self.cls_token_id]
								_a				      =					[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep




				def     UpperCamelCase__  (    self	:   int   ,							__a	:   Any   ,							__a	:   Union[str, Any] = None   ,							__a	:   List[Any] = False   ):


								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__a   ,							token_ids_a=__a   ,							already_has_special_tokens=__a   )

								if token_ids_a is None:
												return [1] + ([0] * len(__a   )) + [1]
								return [1] + ([0] * len(__a   )) + [1, 1] + ([0] * len(__a   )) + [1]




				def     UpperCamelCase__  (    self	:   Union[str, Any]   ,							__a	:   Optional[int]   ,							__a	:   int = None   ):
								_a				      =					[self.sep_token_id]
								_a				      =					[self.cls_token_id]

								if token_ids_a is None:
												return len(cls + token_ids_a + sep   ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]




				@property
				def     UpperCamelCase__  (    self	:   Optional[int]   ):
								return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token




				def     UpperCamelCase__  (    self	:   Any   ):
								_a				      =					{self.convert_ids_to_tokens(__a   ): i for i in range(self.vocab_size   )}
								vocab.update(self.added_tokens_encoder   )
								return vocab




				def     UpperCamelCase__  (    self	:   str   ,							__a	:   str   ):
								return self.sp_model.encode(__a   ,							out_type=__a   )




				def     UpperCamelCase__  (    self	:   Any   ,							__a	:   List[str]   ):


								if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
								_a				      =					self.sp_model.PieceToId(__a   )

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




				def     UpperCamelCase__  (    self	:   Any   ,							__a	:   Optional[Any]   ):


								if index in self.fairseq_ids_to_tokens:
												return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset   )




				def     UpperCamelCase__  (    self	:   int   ,							__a	:   Any   ):
								_a				      =					"".join(__a   ).replace(__a   ,							" "   ).strip()
								return out_string




				def     UpperCamelCase__  (    self	:   Optional[int]   ,							__a	:   List[Any]   ,							__a	:   List[str] = None   ):


								if not os.path.isdir(__a   ):
												logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
												return
								_a				      =					os.path.join(
								    __a   ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

								if os.path.abspath(self.vocab_file   ) != os.path.abspath(__a   ) and os.path.isfile(self.vocab_file   ):
												copyfile(self.vocab_file   ,							__a   )
								elif not os.path.isfile(self.vocab_file   ):
												with open(__a   ,							"wb"   ) as fi:
																_a				      =					self.sp_model.serialized_model_proto()
																fi.write(__a   )

								return (out_vocab_file,)




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Dict 			=		'trocr'
			_UpperCamelCase		:       Union[str, Any] 			=		['past_key_values']
			_UpperCamelCase		:       Union[str, Any] 			=		{
			    'num_attention_heads': 'decoder_attention_heads',
			    'hidden_size': 'd_model',
			    'num_hidden_layers': 'decoder_layers',
			}




			def __init__(				self   ,    snake_case=50265   ,    snake_case=1024   ,    snake_case=12   ,    snake_case=16   ,    snake_case=4096   ,    snake_case="gelu"   ,    snake_case=512   ,    snake_case=0.1   ,    snake_case=0.0   ,    snake_case=0.0   ,    snake_case=2   ,    snake_case=0.02   ,    snake_case=0.0   ,    snake_case=True   ,    snake_case=False   ,    snake_case=True   ,    snake_case=True   ,    snake_case=1   ,    snake_case=0   ,    snake_case=2   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							d_model
						UpperCamelCase__       =							decoder_layers
						UpperCamelCase__       =							decoder_attention_heads
						UpperCamelCase__       =							decoder_ffn_dim
						UpperCamelCase__       =							activation_function
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							dropout
						UpperCamelCase__       =							attention_dropout
						UpperCamelCase__       =							activation_dropout
						UpperCamelCase__       =							init_std
						UpperCamelCase__       =							decoder_layerdrop
						UpperCamelCase__       =							use_cache
						UpperCamelCase__       =							scale_embedding
						UpperCamelCase__       =							use_learned_position_embeddings
						UpperCamelCase__       =							layernorm_embedding

						super().__init__(
						    pad_token_id=snake_case   ,    bos_token_id=snake_case   ,    eos_token_id=snake_case   ,    decoder_start_token_id=snake_case   ,    **snake_case   ,    )


